Automatic dental arch detection and panoramic imaging from cone beam computed tomography dataset

Abstract

Panoramic imaging from the cone beam computed tomography (CBCT) dataset mimics the X-ray beam shooting along a dental arch which represents the teeth and jaw bones. This study aims to propose a novel dental arch detection algorithm in three-dimensional (3D) CBCT space based on the parametric equation to reconstruct panoramic images with clear visualization of jaw bones, teeth, and dental pulps. Our method involves two main steps: dental arch detection and panoramic imaging. The first step detects the dental arch using a parametric equation on a specially found plane in 3D CBCT space. Binary masks of jaw bones and teeth are required to fit the parametric equation. We employ deep learning techniques for tooth segmentation and a traditional method for jaw bone segmentation. In the second step, the maximal intensity projection and ray sum projection are applied for panoramic imaging. In experiments, a total of 20 CBCT datasets are used to evaluate the proposed method. 78 CBCT datasets are used to train the tooth segmentation network. Experiment results show that our proposed method enables us detect the dental arch directly in 3D CBCT space, and provides an accurate, effective, and robust solution for CBCT-based panoramic imaging.